def agent_init(self,taskSpec):
super(ALESarsaAgent,self).agent_init(taskSpec)
self.state_projector = TileCoding(limits=self.limits)
#BasicALEFeatures(num_tiles=np.array([14,16]),
# background_file = '../data/space_invaders/background.pkl')
self.theta = np.zeros((self.state_projector.num_features(),
self.num_actions()))
self.sparse = True
class ALESarsaAgent(ALEAgent):
def __init__(self,alpha=0.1,lambda_=0.9,gamma=.999,eps=0.0,
agent_id=0,save_path='.'):
#use full images and color mode
super(ALESarsaAgent,self).__init__(np.array([0,1,2]),agent_id,save_path)
self.eps = eps
self.name='SARSA'
self.alpha0 = alpha
self.alpha = alpha
self.lambda_ = lambda_
self.gamma = gamma
def agent_start(self,observation):
super(ALESarsaAgent,self).agent_start(observation)
#reset trace
self.trace = np.zeros_like(self.theta)
#action selection
phi = self.get_phi(observation)
vals = self.get_all_values(phi,self.sparse)
a = self.select_action(vals)
#log state and action
self.phi = phi
self.a = a
return self.create_action(self.actions[a])
def agent_init(self,taskSpec):
super(ALESarsaAgent,self).agent_init(taskSpec)
self.state_projector = TileCoding(limits=self.limits)
#BasicALEFeatures(num_tiles=np.array([14,16]),
# background_file = '../data/space_invaders/background.pkl')
self.theta = np.zeros((self.state_projector.num_features(),
self.num_actions()))
self.sparse = True
def get_phi(self,obs):
im = np.array(obs.doubleArray)#self.get_frame_data(obs)#.reshape((210,160))
if self.sparse:
#returns only active tiles indices
return self.state_projector.phi_idx(im)
else:
#returns full binary features vector
return self.state_projector.phi(im)
def update_alpha(self):
pass
def get_value(self,phi,a,sparse=False):
if sparse:
return np.sum(self.theta[phi,a])
else:
return np.dot(phi,self.theta[:,a])
def get_all_values(self,phi,sparse=False):
if sparse:
return np.sum(self.theta[phi,:],axis=0)
else:
return np.dot(phi,self.theta)
def select_action(self,values):
#egreedy
acts = np.arange(values.size)
if np.random.rand()< self.eps:
return np.random.choice(acts)
else:
max_acts = acts[values == np.max(values)]
return np.random.choice(max_acts)
def update_trace(self,phi,a):
self.trace *= self.gamma*self.lambda_
if self.sparse:
self.trace[phi,a] += 1
else:
self.trace[:,a] += phi
#self.trace = np.clip(self.trace,0.,5.)
def step(self,reward,phi_ns = None):
n_rew = self.normalize_reward(reward)
self.update_trace(self.phi,self.a)
delta = n_rew - self.get_value(self.phi,self.a,self.sparse)
a_ns = None
if not (phi_ns is None):
ns_values = self.get_all_values(phi_ns,self.sparse)
a_ns = self.select_action(ns_values)
delta += self.gamma*ns_values[a_ns]
#normalize alpha with nr of active features
alpha = self.alpha / float(np.sum(self.phi!=0.))
self.theta+= alpha*delta*self.trace
return a_ns #a_ns is action index (not action value)
def agent_step(self,reward, observation):
super(ALESarsaAgent,self).agent_step(reward, observation)
phi_ns = self.get_phi(observation)
a_ns = self.step(reward,phi_ns)
#log state data
self.phi = phi_ns
self.a = a_ns
return self.create_action(self.actions[a_ns])#create RLGLUE action
def agent_end(self,reward):
super(ALESarsaAgent,self).agent_end(reward)
self.step(reward)
